Generative Adversarial Networks (GANs) are first proposed in [1]} and have been widely used in various generation and synthesis tasks. A GAN comprises a generator (G) that generates plausible new data and a discriminator (D) that aims to distinguish the generator's fake data from real data. Thus, G and D play a min-max game with each other, leading to the instability of GAN training, such as mode collapse and gradient vanishing. Many efforts have been made on multiple fronts to alleviate these problems [2]}, such as loss functions [3]}, [4]}, [5]}, [6]}, normalization and constraint [7]}, [8]}, conditional techniques [9]}, [10]}, and validation methods [11]}, [12]}. Besides, architecture enhancements have been proven effective to improve GANs performance in many works [13]}, [14]}, [15]}, [16]}, but they all require much expertise and trial-and-error.
